Enhancing Air Quality Forecasting Using Machine Learning Techniques

doi:10.1109/ACCESS.2024.3516883

Enhancing Air Quality Forecasting Using Machine Learning Techniques

2024-01-01

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DOI：10.1109/ACCESS.2024.3516883

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Urbanization is rapidly shaping our world, with more people than ever residing in cities. While cities offer numerous opportunities and conveniences, they also face critical challenges, including air pollution. Addressing these challenges is vital for creating healthier and more liveable urban environments. A transformative solution emerges, bridging the gap between sustainable urban mobility and air quality control through cutting-edge data-driven strategies. Finding a balance between efficient urban living and environmental stewardship is a pressing concern for cities worldwide. In envisioning a future where urban commuting becomes synonymous with eco-friendliness and air quality improvement, a comprehensive platform harnesses the power of data analytics and real-time information to empower commuters and city planners alike. Its intelligent algorithms continuously analyse air quality information, allowing it to predict and address poor air quality. This platform seamlessly integrates with existing urban infrastructure, making it accessible to commuters through user-friendly mobile applications and web interfaces. Commuters can receive personalized recommendations for eco-friendly commuting options. One standout feature is its ability to forecast air quality in urban areas, enabling users to make informed decisions that prioritize their health and environmental sustainability. Encouraging a sense of community among eco-conscious urban residents, it incentivizes sustainable behaviours and offers rewards for reducing emissions. By collecting data on commuting choices and air quality conditions, the platform contributes valuable insights to city authorities for urban planning and pollution control. Representing a paradigm shift in urban living, it aligns individual choices with broader sustainability and air quality goals. It is a testament to the power of technology, data, and community engagement in building smarter, greener, and healthier cities. The proposed approach presents the significance of the system as a transformative solution for sustainable urban living and air quality control, emphasizing the use of cutting-edge technology, data-driven insights, and community engagement to address pressing urban challenges.

Keywords:

Air quality

Urban areas

Real-time systems

Artificial intelligence

Carbon emissions

Renewable energy sources

Biological system modeling

Transportation

Machine learning

Coal

machine learning

artificial intelligence

sustainability

smart city

Enhancing Air Quality Forecasting Using Machine Learning Techniques

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